AI Models
Agents in JetAdmin can use different AI models depending on the task, performance needs, and cost considerations.
Available Models
Anthropic (Claude)
OpenAI
Google (Gemini)
Self-hosted
Claude 4.6 Opus
GPT 5.x (full)
Gemini 3.1 Pro
Llama
Claude 4.5 Opus
GPT 4.x (full)
Gemini 2.5 Pro
Mistral
Claude 4.1 Opus
GPT Mini
Gemini 3.1 Flash
Qwen
Claude 4.5 Sonnet
GPT Nano
Gemini 2.5 Flash
DeepSeek
Claude 4 Sonnet
Codex
Claude 4.5 Haiku
Understanding Model Trade-offs
Faster models are more cost-efficient and ideal for simple, high-volume tasks. More advanced models provide better reasoning, accuracy, and context handling, but consume more credits.
Best for fast responses and high-volume, low-cost tasks.
Model
Used For
Claude 4.5 Haiku
Quick responses, simple automation, chat assistants
Gemini Flash (2.5–3.1)
Lightweight tasks, fast lookups, basic workflows
OpenAI Nano
Short prompts, formatting, simple text handling
Llama (small)
Basic self-hosted tasks with minimal complexity
Balanced performance for most business use cases.
Model
Used For
Claude 4.5 Sonnet
General-purpose agents, multi-step tasks
Gemini Pro (2.5–3.1)
Data analysis, structured workflows
OpenAI Mini
Reliable automation, moderate reasoning tasks
Mistral / Qwen
Mid-level self-hosted workloads and processing
Best for complex reasoning, critical tasks, and high accuracy.
Model
Used For
Claude 4.1 / 4.5 / 4.6 Opus
Deep analysis, complex decision-making
OpenAI 5.x / 4.x (full models, Codex)
Advanced logic, coding, multi-step reasoning
Gemini Pro (latest versions)
Large context tasks, high-accuracy outputs
DeepSeek / large Llama
Advanced self-hosted reasoning and heavy workloads
Choosing Models & Managing Costs
Start with budget or advanced models for most use cases, they are faster and more cost-efficient. Move to expert models only when tasks require deeper reasoning, higher accuracy, or complex logic.
To reduce credit usage:
Use simpler models for repetitive or low-complexity tasks
Keep instructions clear to avoid unnecessary retries
Limit unnecessary tool calls or repeated executions
A good approach is to start simple, test performance, and only scale up when needed.
Last updated
Was this helpful?